Many digital imaging systems enhance the contrast and lightness characteristics of digital images through the application of a tone scale curve. For a generalized tone scale curve ƒ( ), the input pixel value x is transformed to an output pixel value ƒ(x). The shape of the tone scale curve determines the visual effect imparted to the processed digital image. Some tone scale curves applied to digital image are independent of the pixel values in the digital image to be processed. Such image independent tone scale curves are useful for establishing a photographic look to the processed digital images. While image independent tone scale curves can be used to enhance many digital images, digital images that are either too high or low in contrast can benefit from the application of a tone scale curve that is responsive to the distribution of pixel values in the digital image to be processed. For image dependent tone scale curves, the mathematical formula used to generate the function ƒ(x) determines the degree and nature of the image enhancement.
One class of tone scale function generation methods is derived from histogram equalization. A histogram function H(x), i.e. a function of the frequency of occurrence, is calculated from the pixel values x of a digital image. Next the function ƒ(x) is determined for which the histogram function of the processed pixels H(ƒ(x)) will have a particular aim functional W(x). The function ƒ(x) that satisfies this constraint can be calculated given the expression ƒ(x)=kC(W1(H(x))) where the variable k is a normalizing constant. For the special case where the aim functional W(x) is a constant, the expression for the tone scale curve ƒ(x) is given by the expression ƒ(x)=kC(H(x)).
There are many prior art examples of histogram equalization based methods: In commonly-assigned U.S. Pat. No. 4,731,671 Alkofer discloses a method of using a Gaussian function as the aim function W(x). In commonly-assigned U.S. Pat. No. 4,745,465 Kwon discloses a method of generating a tone scale curve also employing a histogram equalization derived method wherein a Gaussian function is used as the aim function W(x). In Kwon's method, the image histogram is calculated by sampling pixels within the image that have been classified as spatially active. An edge detection spatial filter is used to determine the degree of local spatial activity for each image pixel. The local spatial activity measure is compared with a threshold to determine if the pixel value will contribute to the histogram function used to generate the tone scale curve. As with less sophisticated histogram equalization based methods, the methods disclosed by Alkofer and Kwon suffer from inconsistent image enhancement performance. This is principally due to the fact that histogram equalization methods tend to optimize the visualization of image content based on the frequency of occurrence of the corresponding pixel values. As a consequence, extremely bright or dark image areas that are represented by a small percentage of image area can be overwhelmed by more prevalent image areas resulting in tone scale adjusted images that have specular highlights that are rendered too dark and deep shadows that are rendered too light. Therefore, histogram equalization based methods are more suited to image exploitation applications requiring the visualization of image detail than to applications involving the tone reproduction of natural scenes.
In commonly-assigned U.S. Pat. No. 6,285,798 Lee discloses a method of generating a tone scale curve for the purposes of reducing the dynamic range of a digital image. The tone scale curve construction method establishes six constraints and then performs a successive integration procedure to satisfy the constraints. In Lee's method, a dark point determined by the 0.5% image cumulative histogram function value is mapped to a white paper density, a bright point determined by the 99.5% image cumulative histogram function value is mapped to a black paper density, and a mid-point is mapped to itself. Next a shadow slope constraint of greater than 1.0 is imposed at the 0.5% shadow point, a highlight slope constraint of 1.0 is imposed at the 99.5% highlight point, and a mid-tone slope constraint of 1.0 is imposed at the mid-point. Lee states that there are an infinite number of tone scale curves that can satisfy the six constraints. Lee's method constructs a tone scale curve that satisfies the six constraints by assuming an arbitrary initial shape for the tone scale curve and successively convolving the tone scale curve with a Gaussian smoothing function until, upon examination, the tone scale curve satisfies the six constraints to within some acceptable tolerance. Lee's method does not discuss a closed form solution, i.e. a mathematical function that can be evaluated for each point, to the six constraints and therefore must rely the complicated integration procedure. The tone scale curves so constructed are smoothly varying achieving a high slope value at the extremes and at the mid-point with an inflection point between the mid-point and the highlight point and an inflection point between the mid-point and the shadow point. While Lee's method disclosed in commonly-assigned U.S. Pat. No. 6,285,798 can produce smoothly varying tone scale curves, the method does not always converge to a curve that satisfies the six constraints. Furthermore, the Lee's method does not account for the possibility that some digital images require an expansion of the dynamic range of the digital image to achieve enhancement. In addition, the high slope constraints imposed at the extremes and at the mid point of the pixel intensity domain can sometimes lead to a sacrifice of quality for image content corresponding to pixel values that lie between the shadow point and the mid-point, and the mid-point and the highlight point.
In the journal article entitled “Image lightness rescaling using sigmoidal contrast enhancement functions” published in the Journal of Electronic Images Vol. 8(4), p380–393 (October 1999), authors Braun et al. discuss a method of using a single sigmoidal function, e.g. as the integral-of-a-Gaussian function, as a method of generating a tone scale curve that can be used for contrast enhancement of digital images. The sigmoidal function presented by Braun et al. is controlled with a standard deviation and offset parameter which determine the shape of the function. The offset parameter is used to impart lightness changes to digital images while the standard deviation parameter is used to impart contrast changes. While the sigmoidal shaped tone scale curve generation method presented by Braun et al. provides photographically acceptable results, the shape of the function corresponding to shadow and highlight regions of images is not independently controllable. Consequently, for a given digital image, it can be difficult to achieve the desired degree of contrast enhancement while simultaneously achieving the optimum image lightness rendition.